/
det-package.go
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/
det-package.go
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// Copyright (c) 2018-2022 California Institute of Technology (“Caltech”). U.S.
// Government sponsorship acknowledged.
// All rights reserved.
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// * Redistributions of source code must retain the above copyright notice, this
// list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Caltech nor its operating division, the Jet Propulsion
// Laboratory, nor the names of its contributors may be used to endorse or
// promote products derived from this software without specific prior written
// permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
package diffractionDetector
import (
"errors"
"io/ioutil"
"math"
protos "github.com/pixlise/core/v3/generated-protos"
"google.golang.org/protobuf/proto"
)
// Go avoids semicolons, has fixed {} rules (opening { not allowed on its own line), multiple return values
func ParseDatasetProtobuf(path string) (*protos.Experiment, error) {
// Open the dataset
datasetData, err := ioutil.ReadFile(path)
if err != nil {
return nil, err
}
datasetPB := &protos.Experiment{}
err = proto.Unmarshal(datasetData, datasetPB)
if err != nil {
return nil, err
}
return datasetPB, nil
}
func BuildDiffractionProtobuf(dataset *protos.Experiment, diffractionData map[string][]DiffractionPeak) *protos.Diffraction {
diffractionPB := &protos.Diffraction{}
diffractionPB.TargetId = dataset.TargetId
diffractionPB.DriveId = dataset.DriveId
diffractionPB.SiteId = dataset.SiteId
diffractionPB.Target = dataset.Target
diffractionPB.Site = dataset.Site
diffractionPB.Title = dataset.Title
diffractionPB.Sol = dataset.Sol
diffractionPB.Rtt = dataset.Rtt
diffractionPB.Sclk = dataset.Sclk
for loc := range diffractionData {
peakData := make([]*protos.Diffraction_Location_Peak, len(diffractionData[loc]))
for peakID, peak := range diffractionData[loc] {
peakPB := protos.Diffraction_Location_Peak{}
peakPB.PeakChannel = int32(peak.PeakChannel)
peakPB.EffectSize = float32(peak.EffectSize)
peakPB.BaselineVariation = float32(peak.BaselineVariation)
peakPB.GlobalDifference = float32(peak.GlobalDifference)
peakPB.DifferenceSigma = float32(peak.DifferenceSigma)
peakPB.PeakHeight = float32(peak.PeakHeight)
peakData[peakID] = &peakPB
}
locationData := protos.Diffraction_Location{}
locationData.Id = loc
locationData.Peaks = peakData
diffractionPB.Locations = append(diffractionPB.Locations, &locationData)
}
return diffractionPB
}
func SaveDiffractionProtobuf(diffractionPB *protos.Diffraction, fname string) error {
out, err := proto.Marshal(diffractionPB)
if err != nil {
return err
}
if err := ioutil.WriteFile(fname, out, 0644); err != nil {
return err
}
return nil
}
func ParseDiffractionProtoBuf(path string) (*protos.Diffraction, error) {
diffractionData, err := ioutil.ReadFile(path)
if err != nil {
return nil, err
}
diffractionPB := &protos.Diffraction{}
err = proto.Unmarshal(diffractionData, diffractionPB)
if err != nil {
return nil, err
}
return diffractionPB, nil
}
func ScanDataset(dataset *protos.Experiment) (map[string][]DiffractionPeak, error) {
datasetDiffractionPeaksMap := make(map[string][]DiffractionPeak)
for loc := range dataset.Locations {
if len(dataset.Locations[loc].Detectors) == 2 { // this check is to avoid dwell/bulksum spectra
aSpectrum := DecodeZeroRun(dataset.Locations[loc].Detectors[0].Spectrum)
bSpectrum := DecodeZeroRun(dataset.Locations[loc].Detectors[1].Spectrum)
peaks, err := ScanSpectra(aSpectrum, bSpectrum)
if err == nil {
if len(peaks) > 0 {
locationID := dataset.Locations[loc].Id
datasetDiffractionPeaksMap[locationID] = peaks
}
} else {
return nil, err
}
}
}
return datasetDiffractionPeaksMap, nil
}
type DiffractionPeak struct {
PeakChannel int
EffectSize float64
BaselineVariation float64
GlobalDifference float64
DifferenceSigma float64
PeakHeight float64
}
func DecodeZeroRun(encodedSpectrum []int32) []int32 {
fullSpectrum := make([]int32, 4096)
currentIndex := 0
for i := 0; i < len(encodedSpectrum); i++ {
if encodedSpectrum[i] != 0 {
fullSpectrum[currentIndex] = encodedSpectrum[i]
currentIndex++
} else {
currentIndex += int(encodedSpectrum[i+1])
i++
}
}
return fullSpectrum
}
func ScanSpectra(a []int32, b []int32) ([]DiffractionPeak, error) {
if (len(a) != 4096) || (len(b) != 4096) {
return nil, errors.New("a and b spectra lengths are not both of the expected size (4096)")
}
const halfResolution = 15
const minAvgCount = 2.0
const minEffect = 6.0
const minChannel = 500
const maxChannel = 2000
const minHeight = 0.1
potentialPeaks := []DiffractionPeak{}
logA := make([]float64, 4096)
logB := make([]float64, 4096)
for i := range a {
logA[i] = math.Log1p(float64(a[i]))
logB[i] = math.Log1p(float64(b[i]))
}
normalizationFactor := 0.0
allDiffs := make([]float64, 4096)
for i := range a {
allDiffs[i] = logA[i] - logB[i]
}
normalizationFactor = meanFloats(allDiffs)
for i := minChannel; i < maxChannel; i++ {
differences := make([]float64, 2*halfResolution)
for j := i; j < i+2*halfResolution; j++ {
differences[j-i] = (logA[j] - logB[j] - normalizationFactor)
}
meanDiff := meanFloats(differences)
stdDiff := stdFloats(differences, meanDiff)
tStatistic := math.Abs(meanDiff / (stdDiff / math.Sqrt(float64(2*halfResolution))))
peakHeight := math.Abs(differences[halfResolution-1]) + math.Abs(differences[halfResolution]) - math.Abs(differences[0]) - math.Abs(differences[2*halfResolution-1])
meanA := meanInts(a[i : i+2*halfResolution])
meanB := meanInts(b[i : i+2*halfResolution])
avgCounts := 0.5 * (meanA + meanB)
baselineVariation := 0.0
if meanA >= meanB {
meanLogB := meanFloats(logB[i : i+2*halfResolution])
baselineVariation = stdFloats(logB[i:i+2*halfResolution], meanLogB) / meanLogB
} else {
meanLogA := meanFloats(logA[i : i+2*halfResolution])
baselineVariation = stdFloats(logA[i:i+2*halfResolution], meanLogA) / meanLogA
}
if avgCounts >= minAvgCount && tStatistic >= minEffect && peakHeight >= minHeight {
potentialPeaks = append(potentialPeaks, DiffractionPeak{i + halfResolution, tStatistic, baselineVariation, math.Abs(normalizationFactor), stdDiff, peakHeight})
}
}
peaks := pruneNeighbors(potentialPeaks, halfResolution)
return peaks, nil
}
func meanInts(s []int32) float64 {
if len(s) == 0 {
return math.NaN()
}
var sum int32 = 0
for _, v := range s {
sum += v
}
return float64(sum) / float64(len(s))
}
func meanFloats(s []float64) float64 {
if len(s) == 0 {
return math.NaN()
}
var sum float64 = 0
for _, v := range s {
sum += v
}
return sum / float64(len(s))
}
func stdFloats(s []float64, mean float64) float64 {
var std float64 = 0
for _, v := range s {
std += math.Pow(v-mean, 2)
}
return math.Sqrt(std / float64(len(s)-1))
}
func pruneNeighbors(potentialPeaks []DiffractionPeak, boundary int) []DiffractionPeak {
peaks := []DiffractionPeak{}
skip := false
for i := 0; i < len(potentialPeaks); i++ {
localMax := true
for j := 0; j < len(potentialPeaks); j++ {
dist := potentialPeaks[i].PeakChannel - potentialPeaks[j].PeakChannel
if dist < 0 {
dist = -dist
}
if i != j && dist <= boundary && (potentialPeaks[i].EffectSize < potentialPeaks[j].EffectSize) {
localMax = false
} else if i > j && dist <= boundary && (potentialPeaks[i].EffectSize == potentialPeaks[j].EffectSize) {
localMax = true
}
}
if localMax {
peaks = append(peaks, potentialPeaks[i])
} else {
skip = true
}
}
if skip {
return pruneNeighbors(peaks, boundary)
}
return peaks
}